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Estimates and standard error of the estimates

The results published in the NFI tables are estimates for variables in the Swiss forest (population parameters) whose true values are not known and must therefore be extrapolated (estimated) from the NFI sample data.

The extrapolations carry uncertainties. However, the accuracy of the extrapolations can be reliably estimated from the NFI sample, which is designed to be a random sample. To this end, all NFI tables include a second figure in addition to the estimate itself, namely the standard error of the estimate.

In most tables the percent standard error is reported (±%), but the absolute standard error (±) is also reported occasionally, especially for estimated percentages. The relationship between absolute and percent standard error is as follows:

percent standard error = absolute standard error / estimate × 100

absolute standard error = percent standard error × estimate / 100

Information on the forest road network is based on a full survey of all forest roads. In this case, there is no need to specify a standard error because there is no uncertainty caused by sampling.

Confidence interval of the estimate

With the estimate and the standard error of the estimate, the limits of the so-called confidence interval of the estimate can be calculated as:

lower limit = estimate – tQ × absolute standard error

upper limit = estimate + tQ × absolute standard error

If the simple standard error is used for the calculation (tQ = 1), then the 68% confidence interval is formed. In this case, the true value of the population parameter has a 68% probability of lying within this confidence interval of the estimate. If the double standard error is used (tQ = 2), then there is a 95% probability that the true value lies within this 95% confidence interval.

Significance of the estimate

The confidence interval can be used to statistically test whether the estimated population parameter is larger or smaller than a specific reference or target value, or whether two estimated population parameters truly differ (in the real population). For practical applications, proceed as follows: if a reference value lies outside the confidence interval, the estimated population value can be interpreted as being significantly different from it; if it lies within the confidence interval, the difference between the estimated population parameter and the reference value can be interpreted as random or not significant. When two population parameters are compared, they can be interpreted as differing significantly if their confidence intervals do not overlap.

Dealing with missing values

When calculating a results table, data are not always available for all combinations of characteristics^* of the classification variables and regional demarcation applied. In most cases, this indicates that the attribute estimated with the respective target variable does not occur or occurs only very rarely. Usually, the value 0 is used (imputed). However, as this value is not based on direct measurements, the associated standard error is represented with a full stop [.]. If the imputed value of 0 is referred to in the calculation, e.g. in the case of percentages or certain change estimates, no value can be entered. In this case, the estimated value and standard error are both represented with a full stop [.].

For example, no Arolla pines have been found and measured in the Swiss Plateau to date (growing stock of Arolla pines by production region). The values can be assumed to be missing because Arolla pine does not occur in the Swiss Plateau and therefore the growing stock there must be 0.

^* e.g. ‘Arolla pine’ characteristic of the classification variable ‘tree species’ and ‘Swiss plateau’ characteristic of the ‘regional demarcation’

Changes

There are two types of changes in the NFI:

The first type involves specific target variables relating to components of change, such as increment, fellings, mortality and losses. These target variables are only available for two consecutive inventory cycles, e.g. NFI3–NFI4. In the evaluation of components of change, the classification variable characteristic of the second inventory cycle is assigned to the first inventory cycle. These evaluations therefore do not take into account potential changes in a classification variable characteristic from the earlier to the later inventory (e.g. from private to public ownership).

With the second type of change, the difference in target variables, such as number of stems, growing stock or forest area, is used to assess the change between two inventory cycles. Target variables are generally used to represent states, such as that in NFI4, but can also indicate the net change between any two inventory cycles, e.g. NFI1–NFI4. In evaluations of change using these target variables, the change in a classification variable characteristic is considered in the analysis. Consequently, one can detect, for example, that the forest area without shrub forest increased between the two inventory cycles. This only has an effect on classification variables whose characteristic (class) can actually change, e.g. the affiliation with a forest area or the tree condition.

NFI5
number of deadwood stems
deadwood: mechanical strength of wood (3 classes) · deadwood: colonization by shrubs (5 classes)
regional demarcation: economic region
unit: 1000 n
evaluation area: accessible forest without shrub forest
grid: 1.4 km grid, subgrids 1-5
state 2018/26
		economic region
		Western Jura		Eastern Jura		Western Plateau		Central Plateau		Eastern Plateau		Western Pre-Alps		Central Pre-Alps		Eastern Pre-Alps		Northwestern Alps		Central Alps		Northeastern Alps		Southwestern Alps		Southeastern Alps		Southern Alps		Switzerland
deadwood: mechanical strength of wood (3 classes)	deadwood: colonization by shrubs (5 classes)	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%
n/a	n/a	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
	0-1	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	2-10	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	11-20	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	21-50	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	>50	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
fresh/firm wood	n/a	3194	10	900	21	812	22	673	20	1035	19	976	21	2510	15	2140	15	1553	18	674	20	837	23	3258	13	3313	12	6352	10	28229	4
	0-1	1355	16	616	25	325	27	276	39	556	18	451	21	1220	18	761	19	1134	19	308	35	956	33	2732	11	2844	15	3067	13	16601	5
	2-10	0	.	0	.	0	.	0	.	7	100	0	.	0	.	18	100	0	.	0	.	0	.	0	.	7	100	0	.	32	64
	11-20	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	0	.	7	100
	21-50	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	>50	0	.	0	.	0	.	0	.	0	.	0	.	15	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	15	100
	total	4550	9	1517	17	1137	20	950	19	1598	15	1427	18	3745	14	2919	14	2687	15	982	20	1793	22	5990	10	6171	10	9419	9	44884	3
rotten wood	n/a	502	20	62	45	58	56	132	34	194	32	198	36	665	25	722	20	393	22	371	26	199	37	425	21	290	24	757	16	4968	7
	0-1	1246	15	321	25	306	28	362	26	517	24	811	19	1116	16	905	26	1107	21	278	32	491	21	2058	13	1839	15	1309	15	12666	5
	2-10	17	100	0	.	0	.	17	100	0	.	6	100	0	.	0	.	0	.	0	.	0	.	0	.	14	71	6	100	61	46
	11-20	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	21-50	0	.	0	.	0	.	0	.	0	.	0	.	48	73	0	.	0	.	0	.	0	.	0	.	0	.	0	.	48	73
	>50	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	1766	13	383	24	364	26	512	21	711	20	1015	18	1829	14	1627	18	1499	17	649	27	689	21	2483	13	2142	14	2072	11	17742	4
mouldering/mull wood	n/a	176	42	7	100	20	74	37	77	7	100	33	52	185	34	116	46	89	43	110	47	39	61	79	45	51	55	283	33	1232	14
	0-1	981	15	101	46	196	34	301	22	375	25	950	24	970	17	630	22	699	29	211	32	229	29	1194	16	1183	14	1193	16	9212	6
	2-10	0	.	0	.	0	.	0	.	17	100	6	100	34	65	9	100	21	100	0	.	7	100	0	.	38	55	0	.	132	32
	11-20	0	.	0	.	0	.	0	.	0	.	0	.	0	.	9	100	7	100	0	.	0	.	0	.	0	.	0	.	16	71
	21-50	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	>50	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	1156	14	108	44	216	33	337	21	399	25	989	23	1189	16	764	21	816	26	320	29	275	29	1274	15	1272	13	1476	15	10592	5
total	n/a	3873	10	969	21	890	21	843	17	1236	17	1207	18	3361	14	2978	13	2035	15	1155	18	1075	22	3763	12	3654	11	7392	9	34429	4
	0-1	3582	10	1038	19	827	20	939	19	1448	14	2212	17	3306	12	2296	18	2940	16	797	24	1676	21	5984	10	5866	11	5569	9	38479	4
	2-10	17	100	0	.	0	.	17	100	24	77	12	71	34	65	27	75	21	100	0	.	7	100	0	.	59	41	6	100	224	24
	11-20	0	.	0	.	0	.	0	.	0	.	0	.	0	.	9	100	7	100	0	.	0	.	0	.	7	100	0	.	23	58
	21-50	0	.	0	.	0	.	0	.	0	.	0	.	48	73	0	.	0	.	0	.	0	.	0	.	0	.	0	.	48	73
	>50	0	.	0	.	0	.	0	.	0	.	0	.	15	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	15	100
	total	7472	8	2007	16	1717	17	1799	14	2708	12	3431	15	6763	11	5310	13	5002	12	1951	18	2758	18	9747	9	9585	9	12967	7	73219	3

Table citation

Abegg, M.; Ahles, P.; Allgaier Leuch, B.; Cioldi, F.; Didion, M.; Düggelin, C.; Fischer, C.; Herold, A.; Meile, R.; Rohner, B.; Rösler, E.; Speich, S.; Temperli, C.; Traub, B.,
2023: Swiss national forest inventory - Result table No. 1411561. Birmensdorf, Swiss Federal Research Institute WSL

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